Design Guidelines of Mobile Apps for Older Adults: Systematic Review and Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Mobile apps are fundamental tools in today's society for practical and social endeavors. However, these technologies are often not usable for older users. Given the increased use of mobile apps by this group of users and the impact that certain services may have on their quality of life, such as mobile health, personal finance, or online administrative procedures, a clear set of guidelines for mobile app designers is needed. Existing recommendations for older adults focus on investigations with certain groups of older adults or have not been extracted from experimental results. OBJECTIVE: In this research work, we systematically reviewed the scientific literature that provided recommendations for the design of mobile apps based on usability testing with older adults and organized such recommendations into a meaningful set of design guidelines. METHODS: We conducted a systematic literature review of journal and conference articles from 2010 to 2021. We included articles that carried out usability tests with populations aged >60 years and presented transferable guidelines on mobile software design, resulting in a final set of 40 articles. We then carried out a thematic analysis with 3 rounds of analysis to provide meaning to an otherwise diverse set of recommendations. At this stage, we discarded recommendations that were made by just 1 article, were based on a specific mobile app and were therefore nontransferrable, were based on other authors' literature (as opposed to recommendations based on the results of usability tests), or were not sufficiently argued. With the remaining recommendations, we identified commonalities, wrote a faithful statement for each guideline, used a common language for the entire set, and organized the guidelines into categories, thereby giving shape to an otherwise diverse set of recommendations. RESULTS: Among the 27 resulting guidelines, the rules Simplify and Increase the size and distance between interactive controls were transversal and of the greatest significance. The rest of the guidelines were divided into 5 categories (Help & Training, Navigation, Visual Design, Cognitive Load, and Interaction) and consequent subcategories in Visual Design (Layout, Icons, and Appearance) and Interaction (Input and Output). The recommendations were structured, explained in detail, and illustrated with applied examples extracted from the selected studies, where appropriate. We discussed the design implications of applying these guidelines, contextualized with relevant studies. We also discussed the limitations of the approach followed, stressing the need for further experimentation to gain a better understanding of how older adults use mobile apps and how to better design such apps with these users in mind. CONCLUSIONS: The compiled guidelines support the design of mobile apps that cater to the needs of older adults because they are based on the results of actual usability tests with users aged >60 years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.222 | 0.402 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.060 | 0.033 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".